Artificial neural networks are often used in conjunction with other functions of a linear type for the treatment and processing of data, such as for example filtering of signals, convolution, or similar.
The combination of these functions can be carried out by means of a software program suitable to operate on sequential processors.
This solution, however, is now outdated since it has been noticed that using optimized architectures dedicated to the various and specific functions better performance is obtained, both in terms of speed of calculation and in terms of accuracy
For artificial neural networks, which require non-linear transfer functions, specific electronic devices are normally used suitable to calculate the relative transfer function, which can be of various type, and which typically consists of a sigmoid, Gaussian, step or ramp function or otherwise; as for the filtering or other pre-treatment of data, which on the contrary require linear transfer functions, digital signal processors (called DSP) are normally used, operating singly or in parallel.
The architectures which derive from this configuration are in any case very complex from the point of view of the circuits and often perform their calculations slowly or not in optimized manner.
The present Applicant has devised and embodied this invention to overcome these shortcomings of the state of the art, and to obtain further advantages.